Correction of a halo in a digital image and device for implementing said correction

ABSTRACT

The object of the invention is a method ( 400 ) for correcting a halo (H) in a digital image ( 1 ) captured using photogrammetry in a 3-D modeling studio, the halo being generated through the interaction of light originating from a light source (L 3,  L 4,  L 5,  L 6 ) in the studio with the optic of the shooting device, and manifesting as a local lightening of the digital image, the method comprising the steps of generating ( 410 ) a light intensity map (M) characterizing the light source in terms of spatial distribution and light intensity, providing ( 420 ) a convolution kernel specific to the shooting device, calculating ( 430 ) a convolution product of the light intensity map and the kernel to obtain a corrective value map (CVM), and removing the corrective value map from the digital image pixel by pixel to produce a corrected image (Icorr) in which the halo is not present.

TECHNICAL FIELD

The invention relates to a method for correcting a halo appearing in adigital image captured during photo or video shooting and an imagecapture device intended to implement said halo correction method.

The invention relates more particularly to a method intended to correctthe halo effect created by light sources in view of a shooting deviceand reducing the quality of an image captured by this device byinhomogeneous modification of its brightness.

PRIOR ART

In order to take shots of a scene in good light conditions, artificiallighting systems are conventionally used to illuminate this scene.

Due to the intensity of the light produced by these lighting systems andthe interaction of these lights with the shooting devices, artifacts maybe generated in the images captured by these devices, manifesting asdiffuse or localized lightenings negatively impacting their quality, inparticular by lowering their contrast and by generating inhomogeneousmodifications of their brightness such as whitening, flare factors ordiffraction figures.

In the case of conventional shots with, for example, a single cameraused as the shooting device, the light sources are generally placedoutside the field of the camera so as to avoid generating suchartifacts, for artistic purposes.

Furthermore, visual corrections such as contrast enhancements areroutinely implemented, also for artistic purposes, i.e. according tosubjective criteria.

In the case of a three-dimensional scene modeling studio by multi-viewphotogrammetry as described in patent documents FR3016028 B1 andWO2019/166743 A1, the light sources and the cameras are distributedaround a scene to be modeled, and it is generally impossible to avoidthe presence of light sources in the fields of the cameras, leading tothe above-mentioned problems of degradation of the quality of the imagescaptured by these cameras.

The three-dimensional scene modeling, based on a three-dimensionalanalysis of the images, is therefore also negatively impacted by theabove-mentioned artifacts.

Disclosure of the Invention

One objective of the invention is to correct the artifacts caused by thelight sources used during a shot, all of these artifacts beingdesignated by the term “halo,” so as to render the digital imagescaptured by a shooting device much as they would have been without thebrightness distortions caused by the halo.

To this end, the object of the invention is a method for correcting ahalo in a digital image to be corrected of a scene captured in athree-dimensional modeling studio using photogrammetry by means of ashooting device having a shooting field of the scene, this halo beinggenerated through the interaction of light emitted by a light sourcewith the optic of the shooting device, and manifesting as a lighteningof pixels of the digital image to be corrected, said light sourceforming part of a lighting system of the scene, the method comprisingthe steps of generating a digital light intensity map characterizingsaid light source in terms of spatial distribution relative to theshooting field and in terms of light intensity as it is perceived fromthe shooting device during the capture of the image to be corrected,this map forming a first data matrix, providing a convolution kernelspecific to said shooting device, forming a second data matrix,calculating a convolution product of the first matrix and the secondmatrix to obtain a third data matrix corresponding to a corrective valuemap of the halo in the digital image to be corrected, and removing thecorrective value map from the digital image to be corrected pixel bypixel to obtain a corrected image in which the halo is not present.

A first advantage of the invention is the correction of a haloinhomogeneously modifying the brightness of an image captured by ashooting device, this halo manifesting by one or more local lighteningsor whitenings in the image when it is not corrected.

The method according to the invention thus very significantly improvesthe contrast and the reproduction of the colors and the light intensitylevels not only within the same image, but also, in the case of a studiocomprising several shooting devices, between images captured by thesedifferent devices.

In addition, in the case of three-dimensional modeling of a scene bymulti-view photogrammetry, the modeling is based, inter alia, on thecolor identity of a given element of a scene to identify it and trackits movement from images captured by multiple shooting devices.

The better appreciation of the colors in the images processed by themethod according to the invention therefore allows betterthree-dimensional modeling of a scene.

The method according to the invention may have the following specificfeatures:

-   -   the step of generating the digital light intensity map may        comprise the steps of generating a preliminary digital light        intensity map characterizing said light source in terms of        spatial distribution relative to the shooting field and in terms        of light intensity as it is perceived from the shooting device        when said light source is fully visible to the shooting device,        and generating said digital light intensity map from the        preliminary digital map and the digital image to be corrected,        by determining pixels belonging to said light source in the        preliminary digital map that are hidden from the shooting device        in the digital image to be corrected;    -   the convolution kernel may be a matrix generated from a sum of a        one-dimensional function having a constantly decreasing envelope        and representative of contributions from kernel scattering        phenomena and a two-dimensional function and representative of        contributions from a kernel diffraction figure;    -   the convolution kernel can be a matrix generated from a        constantly decreasing isotropic function;    -   the convolution kernel can be generated by the steps of        acquiring a first and a second digital training image by means        of the shooting device, these two images respectively comprising        a training light source that is switched off and said training        light source that is switched on, generating a training light        intensity map of the training source from the first digital        training image by assigning a light intensity value to the        pixels of this second digital image comprised in the light        source, and calculating the kernel from the two digital training        images and the light intensity map.

The invention extends to an image capture device for a three-dimensionalmodeling studio, comprising a plurality of shooting devices functionallyconnected to a data processing unit, in which said data processing unitis specially adapted to implement the method for correcting a haloaccording to the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be better understood and other advantageswill appear on reading the detailed description of the non-limitingembodiment taken by way of example and illustrated by the appendeddrawings, which are briefly described below.

FIG. 1 illustrates a horizontal sectional view of a three-dimensionalscene modeling studio by multi-view photogrammetry.

FIG. 2 illustrates a method of establishing a light intensity map.

FIG. 3 illustrates a variant of the method shown in FIG. 2 .

FIG. 4 is a diagram of the method for correcting a halo in an image.

FIG. 5 illustrates a function for generating a convolution kernel forcorrecting a halo.

FIG. 6 illustrates a convolution kernel in the form of a matrix.

FIG. 7 is a diagram of a method for determining a convolution kernel.

FIG. 8 illustrates the method of FIG. 7 .

FIG. 9 is a diagram of a method for determining a convolution kerneldifferent from that of FIG. 7 .

FIG. 10 illustrates a device for implementing the methods of thediagrams of FIGS. 4, 7 and 9 .

DESCRIPTION OF AN EMBODIMENT OF THE DEVICE ACCORDING TO THE INVENTION

This embodiment is described by FIGS. 1 to 10 and relates to theapplication of the invention to a three-dimensional scene modelingstudio by multi-view photogrammetry.

The studio comprises cameras C1 to C8 used as shooting devices, arrangedaround an area A where scenes to be modeled from digital images capturedby these cameras are placed.

The studio further comprises light sources L1 to L8 also regularlyarranged around the area A.

The field V of the camera C1 encompasses the light sources L4 and L5,which, due to their high light intensity and their interactions with theoptical elements of the camera C1 (lenses, possibly diaphragm), aresources of artifacts in the captured images, considered collectively asa halo inhomogeneously lightening an image captured by the camera C1.

This embodiment consists in correcting a digital image of a scenecaptured by the camera C1.

Image Correction

The method for correcting an image according to the invention,illustrated by the diagram 400 of FIG. 4 , consists in modeling the halogenerated by the light sources in an image to be corrected captured bythe camera C1, then in removing it from this image so as to obtain acorrected image freed from the halo.

For the sake of simplification, here we consider the case of an image ingrayscale, considering that the method applies in the same way to colorimages, simply by processing the different color channels in parallel,such as a red channel, a green channel and a blue channel.

Step 405 consists in capturing a digital image I to be corrected bymeans of the camera C1 and storing it in computer memory, the halo Hlightening a certain region of the image, as illustrated by thecrosshatched region of FIG. 2 , diagram (b).

Step 410 consists in generating a digital light intensity map Mindicating spatial distributions and intensities of the light sourceslocated in view of the camera C1.

In a first example of this embodiment, only the halo generated by thelight-emitting surfaces of the light sources directly visible by thecamera C1, L4 and L5 is corrected.

Each visible light source of the camera can be considered a set ofelementary light sources each visible in the image to be corrected inthe form of a saturated pixel.

“Saturated pixel” means a pixel displaying a level of brightness at themaximum value, 255 in the case of a luminosity coded on 8 bits in adigital image.

Diagram (c) of FIG. 2 illustrates the light sources L4 and L5 located inthe field V of the camera C1 as seen by the camera C1 during the captureof the image to be corrected, pixel by pixel, the source L4 beingpartially hidden from the camera C1 by the character CE of the sceneshown in diagram (b).

This diagram (c) here corresponds exactly to an image of the field V ofthe camera C1 according to a horizontal extent E corresponding to thehorizontal extent E of the field of the camera C1.

Step 410 can then comprise sub-steps 412, 414 and 416.

Sub-step 412 consists in generating a digital light intensity map of theempty studio, that is to say, with the light sources entirely visiblefrom the camera C1, by determining the saturated pixels of an image ofthe empty studio with the light sources L4 and L5 switched on, thenassigning these pixels the light intensity of the light sources, so asto arrive at a preliminary digital light intensity map PM.

Diagram (a) of FIG. 2 illustrates the preliminary map PM, correspondingto an image of the field V of extent E of the camera C1 comprising onlythe light sources L4 and L5 as seen by the camera C1, these sources herebeing rectangular illuminated surfaces.

This light intensity map is a first matrix of pixels that correspond interms of position to the pixels of the digital image I to be corrected,the brightness value of the light sources being assigned to the pixelsof the map belonging to a light-emitting surface seen by the camera C1,the other pixels having an essentially zero value.

The light intensity here relates to the maximum brightness achievable bya saturated pixel in the image to be corrected.

On an arbitrary basis of luminosity consisting in assigning 1 as thelight intensity value that is just sufficient to saturate a pixel of animage to be corrected, the brightness of the light sources can reachseveral thousands, 10,000 for example, and can be measured byconventional means.

For example, by adjusting the light sensitivity range of the camera C1,said camera can directly measure the light intensity of the lightsources pixel by pixel while avoiding saturating the pixels, and thusgenerate the light intensity map PM, which constitutes a referencedigital image of the shooting device's field.

By proceeding in this manner, only the light sources located in thefield V of the camera C1 are taken into account, which are also thelight sources generally degrading the image to be corrected the most.

It is also possible to manually enter the positions and intensities ofthe light sources in a computer file in order to generate a lightintensity map of the empty studio.

Sub-step 414 consists in identifying, by conventional image processingmeans, the pixels of the map PM belonging to a light source that arefound as unsaturated pixels in the image to be corrected I, illustratedby FIG. 2 , diagram (b).

This step makes it possible to identify the pixels of the illuminatedsurfaces of the preliminary map PM that are hidden from the camera C1 byan element of a scene, such as the character CE of FIG. 2 , diagram (b).

Sub-step 416 consists in assigning an essentially zero value to thepixels identified in step 414 so as to obtain the light intensity map Millustrated by FIG. 2 , diagram (c), representative of the light sourcesdirectly seen by the camera C1 during the acquisition of the image I tobe corrected, indicating their respective positions and lightintensities in the field of the camera.

The light sources can have spatially inhomogeneous intensities in thelight intensity map M, for example to reflect a situation of a sourceconsisting of a panel of very bright light-emitting diodes placed behinda diffuser panel only imperfectly diffusing the light from the diodes,letting them appear in the form of small localized surfaces having lightintensities stronger than their surroundings.

Step 420 of diagram 400 consists in providing a convolution kernel Kadapted to the camera C1 this kernel can be considered a halo generationkernel specific to the camera C1, this kernel forming a second datamatrix.

This kernel is a matrix translating the influence of a point of a lightsource on the brightness of each point of the image captured by thedigital image sensor of the camera and which is to be corrected.

The camera is characterized by its optical system (lenses, diaphragm)and its sensor, formed by a matrix of photosensitive pixels.

The kernel can be defined as a matrix in which each element translatesthe influence of a pixel of a digital light intensity map on the pixelsof a digital image to be corrected, as explained below.

It is recalled that a digital image is a data matrix, each data itemexpressing a light intensity of the corresponding pixel of the image.

The halo is primarily noticeable only when the light source is strongrelative to the light intensity level of the scene from which an imageis being captured, which is the case with studio light sources.

Furthermore, each pixel of the light intensity map belonging to a lightsource can be considered an elementary light source generating its ownhalo.

However, the overall halo of the image to be corrected results from anadditive phenomenon, the effects of each light source or each region ofa light source being added to those of the other light sources orregions of light sources.

Thus, the halo of the digital image to be corrected is the addition ofall the halos specific to each of the pixels of the digital map M, andcan be calculated by the convolution, or convolution product, of thelight intensity map and a convolution kernel K modeling the effects ofan elementary light source on the capture of an image by the camera.

For this reason, even if the halo generated by an elementary surface ofa light source remains localized and barely perceptible, the halogenerated by the light sources considered as a whole has a much greaterimpact and range, which must be taken into account by the kernel.

The inventors thus have the experience of light sources located in animage to be corrected generating a significant halo phenomenon over aradius equivalent to a quarter of the width of the image, so that insuch a situation the kernel will have to be twice the normal size,therefore equivalent to half the size in pixels of the image.

For the case of a square kernel and an image 2,000 pixels wide, thekernel could thus correspond to a two-dimensional matrix of 1,000×1,000elements, the effects of the halo extending over a radius of 500 pixelsfrom a considered light source, such as a pixel of an image of anilluminated surface of an artificial lighting system.

The halo whose effects one seeks to correct is generated by theinteraction of one or more light sources with the optics of the cameracapturing the images to be corrected, in particular the lenses and thediaphragm, and is therefore specific to the optical system of the cameraused.

We can consider the simple, but broadly applicable, case of an isotropichalo generated by the multiple scattering of the light received by thelenses of the camera optic, resulting in an inhomogeneous lightening ofthe image captured by the latter.

The effect of the isotropic halo on a pixel of the image decreasesrapidly with the apparent distance of this pixel from a given pixel of alight source and depends solely on this distance, since here we arelimiting ourselves to an isotropic effect.

It is possible to empirically determine an adequate kernel to correctthe image affected by this isotropic halo based on an isotropicparametric function, first rapidly decreasing then tendingasymptotically toward 0, which translates the local, isotropic andrapidly decreasing influence of a pixel from a light source on an imageto be corrected.

FIG. 5 generically shows such a parametric function PF defined usingparameters and varying as a function of a radius r that represents, inthe case of our kernel, the distance from the considered light source.

FIG. 6 illustrates a convolution kernel K, i.e. a matrix modeling theeffect of a light source on the image for a particular camera, derivedfrom the function of FIG. 5 by spatial digitization on a number ofpoints corresponding to a distance from the sensor of the camera C1 onwhich the halo has a perceptible influence (1,000 points in the exampleabove).

The elements of this matrix are defined so as to have values varyingwith their distance from the center of the matrix according to theparametric function PF, each datum of the matrix corresponding to apixel and being considered to be distant from its nearest neighbors by adistance equal to the pixel pitch of the camera sensor.

The kernel K of FIG. 6 illustrates this construction, with data ofvalues decreasing with their respective distances with respect to thecenter of the matrix according to the decreasing parametric function PF.

For illustration, the central datum of the kernel here has a value of 1and the data furthest from the center have a value of 10⁻¹⁵, but this ofcourse only represents a particular case taken as an example.

By applying this kernel to the image correction as described below, thenmanually adjusting the parameters of the parametric function PF in viewof the results of the correction to adapt it to the shooting system usedand to improve the correction, the kernel is modified by successiveiterations until a satisfactory level of correction is obtained.

Due to the particular situation represented by each shooting system, itis not possible to define one or more precise parametric functions thatare applicable to all systems, such that the adjustment phase describedabove is essential unless the kernel training method described below isapplied.

Each practitioner may choose one or more types of parametric functions,depending on the type of camera he uses and his personal experience andpreferences in terms of calculation.

Step 430 of the image correction method consists in calculating acorrective value map CVM illustrated by the image of diagram (d) of FIG.2 , each pixel of which corresponds to a pixel of the image to becorrected, by obtaining the convolution product of the light intensitymap M and of the convolution kernel K, this map forming a third datamatrix.

The map CVM comprises correction regions Corr with non-zero valuescorresponding to the halo generated by the light sources of the lightpower map M, the pixels of the other regions of the map CVM having zero,non-corrective values.

Step 440 of the method consists in calculating a corrected image lcorrillustrated by the diagram (e) of FIG. 2 , by removing the correctivevalue map CVM from the image I to be corrected, pixel by pixel, toobtain a corrected image in which the halo is not present.

It is understood that the expression “to obtain a corrected image inwhich the halo is not present” expresses an ideal objective that cannotin practice be completely achieved, that is to say, complete eliminationof the halo is the objective, but the implementation and the advantagesof the method do not require perfect elimination of the halo from theimage to be corrected.

In this document, removing a first image from a second image amounts toremoving, for each pixel, the brightness values of the first image fromthe respective brightness values of the second image.

Finally, step 450 consists in recording the corrected image calculatedin step 440 in computer memory.

FIG. 3 shows a variant in the establishment of a light intensity map M′,differing from the method of FIG. 2 in that the light intensity map isan image of arbitrary size of extent E′ preferably covering a widerfield than that of the camera C1 so as to encompass the latter.

It is for example possible to manually enter the positions of the lightsources included in this extent E with respect to the field of view ofthe camera C1, to insert them into an image having a surface in pixelsencompassing the image to be corrected, then assign to these positionslight intensities measured in a conventional manner in order to obtainthe preliminary light intensity map PM′ illustrated by diagram (a) ofFIG. 3 .

For reasons of convenience for the calculations, each pixel of the imageI to be corrected preferably corresponds exactly to one pixel of thepreliminary map PM′.

From a practical point of view, the map PM′ can also be obtained bychanging the orientation of the camera C1 to capture views of the entireextent E′ with an adequate range of light sensitivity, then by combiningthese views in a conventional manner to form the preliminary map PM′.

It is then possible, by implementing sub-steps 414 and 416, to establisha light intensity map M′, illustrated by FIG. 3 , diagram (b), adaptedto the correction of the image to be corrected I of FIG. 2 , diagram(b).

This map, of extent E′ greater than extent E, takes into account notonly the light sources L4 and L5 located in the field of the camera C1,but also the light sources L3 and L6 located outside the field V of thecamera C1 and therefore not visible in the image I to be corrected, butliable to contribute significantly to the halo degrading the quality ofthe latter.

By using this light intensity map in the correction method 400, it ispossible to correct a halo generated not only by the light sourcescomprised in the image to be corrected, but also by light sourceslocated outside the image to be corrected, thus improving the quality ofthe correction.

Kernel—Training Method

The image correction procedure described above can use a convolutionkernel obtained empirically as explained previously, or else a kernelobtained by calculation by means of a training procedure.

We will outline two possible training procedures, each making itpossible to calculate a convolution kernel for generating a halo for agiven shooting device, by means of two training images captured by thisshooting device and respectively representing a light source that isswitched on and the same light source switched off.

These procedures are based on the principles according to which one ofthese two images makes it possible to determine a light intensity map asdefined above and the difference between these images makes it possibleto determine the halo created by the light source interacting with theconsidered shooting device.

From these data, and knowing how the halo is generated from the lightpower map and the desired convolution kernel, it is possible to useconventional calculation methods to find this kernel.

A first training procedure is illustrated by the diagram 700 of FIG. 7and the diagrams (a) to (f) of FIG. 8 and implements a deconvolution.

Step 710 consists in capturing a first training digital image LI1encompassing a switched off light source Soff, identified by Soff inFIG. 8 , diagram (a), using the shooting system for which one wishes tocalculate a halo generation kernel.

It may be the shooting device used to capture an image to be corrected,or another device of the same model equipped with the same optic.

Step 720 consists in capturing a second training digital image L12encompassing the same light source, but switched on this time, under thesame conditions as the image captured when it was switched off,identified by Son in FIG. 8 , diagram (b).

It should be noted that, due to the halo caused by the light from thelight source, the area that it occupies will appear larger in the imagescaptured when it is switched on than when it is switched off.

Step 730 consists in removing the first image LI1 from the second imageLI2, which makes it possible to obtain a third digital image LI3comprising portions representing the halo generated by the interactionof the light source with the shooting device.

In diagram (c) of FIG. 8 , the regions Hint and Hext respectivelyrepresent halo regions located inside and outside the switched off lightsource of diagram (a) of FIG. 8 .

Step 740 consists in extracting a curve LC from the third image LI3,this curve LC representing the difference in brightness ΔL between theimages LI1 and LI2 along a segment Seg crossing the light source, asillustrated by FIG. 8 , diagram (d).

It is then considered that a portion P of the curve LC corresponding toan outer periphery of the light source, crossing the region Hext andincluding a maximum of the curve LC, is representative of the halo andmakes it possible to find the kernel.

Conversely, the region Hint is not considered to provide informationthat is usable in practice to determine the kernel.

Step 750 consists in generating a light intensity training map LM,illustrated by FIG. 8 , diagram (e), for example from the first imageLI1 by assigning the pixels considered to be part of the light source alight intensity value of the source measured in a conventional manner.

For example, a pixel can be considered part of the light source when itexceeds a given light intensity level, this given light intensity levelbeing chosen by an operator so as to distinguish the switched off lightsource from the background of the image LI1.

The training light intensity map LM could also be obtained manually asdescribed above for obtaining the preliminary light intensity map PM.

From the portion P of the curve LC and the training light intensity mapLM, it is possible to determine a kernel generation function KF, thenthe desired convolution kernel KM itself in the form of a matrix, whichwill be used subsequently in the image correction method outlined above.

More specifically, considering that the function represented by thecurve LC in the region P is the result of the convolution of thetraining light intensity map LM and of the kernel that it is sought toobtain, a deconvolution operation 760 of the curve LC by the map LMmakes it possible to determine a kernel generation function KF,deconvolution done by means of a digital data processing unit such as acomputer processor.

Finally, step 770 for spatial digitization of the function KF on anumber of points corresponding to a distance in pixels from the sensorof the shooting device on which the halo has a perceptible influencemakes it possible to determine the kernel, that is to say, the elementsof the matrix KM.

The elements of the matrix KM are defined from the function KF as thekernel K in FIG. 6 as derived from the function PF in FIG. 5 , so as toobtain the matrix KM in FIG. 8 , diagram (g), with data having values bto f decreasing in this order with their respective distances withrespect to the center of the matrix occupied by the element a, accordingto the decreasing function KF.

A second training procedure is illustrated by the diagram 900 of FIG. 9and makes it possible to approximate a kernel generation function byconventional mathematical methods of approximations by regression suchas approximations by polynomial regression.

For the steps of the diagram 900 having the same identifiers as thesteps of the diagram 700, reference can be made to the precedingexplanations.

It is considered that the image LI3 obtained from step 730 correspondsto the halo generated by the switched on light source Son.

However, this halo is modeled by the convolution product of theconvolution kernel sought and of the light intensity map LM obtained atthe end of step 750.

It is therefore understood that it is possible to find the convolutionkernel sought by successive approximations of the kernel aimed atcausing the result of the convolution product and the image LI3 toconverge.

Concretely, it is possible to provide an initial kernel in step 910, toobtain its convolution product with the light intensity map LM duringstep 920, then to compare the obtained convolution product with theimage LI3 during a test step 930.

If, according to criteria chosen by the practitioner, it is determinedduring the test step that the image LI3 resulting from step 730 and theresult of the convolution product from step 920 are too far apart, a newkernel is calculated by means of a data processing unit according toconventional regression methods during step 940, which is reinjectedinto the convolution product in place of the kernel of step 910, thensteps 920 to 940 are repeated in a loop, until the test step 930indicates sufficient convergence of the convolution product toward theimage LI3.

Once sufficient convergence has been obtained, during step 950 the lastcalculated kernel is recorded in a computer memory as the desiredconvolution kernel, similar to the kernel KM obtained by the methodillustrated by the diagram 700 and FIGS. 7 and 8 .

The examples above are limited to cases of isotropic halos generated bya kernel that is in turn isotropic, the kernel being calculated by meansof a one-dimensional function representing the light intensityvariations only in one direction, the radial direction, and which issufficient to characterize the isotropic contributions to the halo.

Such an isotropic kernel is generally sufficient, since one may oftenneglect, for example, the diffraction effects of the diaphragm, whichare effectively negligible for a diaphragm using a large aperture.

In the case of non-negligible diffraction from the diaphragm, one mayadd to a one-dimensional function, representing the isotropiccontributions to the kernel, a two-dimensional function having asymmetry on the same order as the diaphragm to represent thecontributions of its diffraction to the kernel, then proceed in the sameway as before for an empirical determination or a determination bytraining the kernel.

This principle applies to any type of optical effect participating inhalo generation in an image to be corrected.

In general, the convolution kernel can be generated from a sum of aone-dimensional function having a constantly decreasing enveloperepresentative of isotropic contributions to the kernel and of atwo-dimensional function representative of anisotropic contributions tothe kernel.

The isotropic contributions to the kernel may in particular come fromscattering phenomena by the lenses of the considered shooting device.

The anisotropic contributions to the kernel may in particular come fromdiffraction phenomena, visualized for example in the form of diffractionfigures in an image to be corrected.

In such a case, the two-dimensional function may correspond to afunction representative of a diffraction figure and having the sameorder of symmetry as this figure, or may correspond to a sum of suchfunctions.

FIG. 10 illustrates an image capture device 100 for a three-dimensionalmodeling studio that is adapted to implement the halo correction methodaccording to the invention.

This device comprises a plurality of cameras C used as shooting devices,each connected to a digital data processing unit DTU comprising a datacentralization and calculation unit CU and decentralized units DUforming the interface between the data centralization unit CU and eachof the cameras C.

A control monitor MON connected to the centralization unit makes itpossible to view the images captured by the system and a digital dataentry unit KB such as a numeric keyboard makes it possible to enterorders in the data processing unit.

The monitor MON and the unit KB can be used, for example, to refine thekernel employed by the halo correction method in the case of empiricaldetermination of the kernel by visual estimation, by an operator, of thequality of the halo correction in the images and manual modification ofthe parameters of the kernel generation function by this operator.

In this example, each of the decentralized units DU comprises a memoryin which the convolution kernel adapted to the model of the cameras ofthe device are stored.

Light intensity maps are also stored in these decentralized units, eachspecific to a camera, associated with the configuration of the modelingstudio in which the device is integrated and depending on thearrangement of the concerned cameras and the lighting systems.

These decentralized units are arranged to process the images captured bymeans of the halo correction method according to the invention, usingthe light intensity maps and the kernel stored in memory.

Of course, this system can also be adapted to the implementation of atraining procedure of the convolution kernel.

It should be noted that the images mentioned in this description may ormay not have undergone digital processing intended, for example, toimprove their contrast or sharpness, and may each be understood as asingle digital image captured at a given instant, or as an average ofseveral digital images captured at different instants.

It goes without saying that the present invention is not restricted tothe embodiment described above, and may be modified without departingfrom the scope of the invention.

1. A method for correcting a halo in a digital image to be corrected ofa scene captured in a three-dimensional modeling studio usingphotogrammetry by means of a shooting device having a shooting field ofthe scene, this halo being generated through the interaction of lightemitted by a light source with the optic of the shooting device, andmanifesting as a lightening of pixels of the digital image to becorrected, said light source forming part of a lighting system of thescene, comprising: generating a digital light intensity map wherein saidlight source in terms of spatial distribution relative to the shootingfield and in terms of light intensity as it is perceived from theshooting device during the capture of the image to be corrected, thismap forming a first data matrix; providing a convolution kernel specificto said shooting device, forming a second data matrix; calculating aconvolution product of the first matrix and the second matrix to obtaina third data matrix corresponding to a corrective value map of the haloin the digital image to be corrected; and removing the corrective valuemap from the digital image to be corrected pixel by pixel to obtain acorrected image in which the halo is not present.
 2. The method forcorrecting a halo according to claim 1, wherein the step of generatingthe digital light intensity map comprises: generating a preliminarydigital light intensity map wherein said light source in terms ofspatial distribution relative to the shooting field and in terms oflight intensity as it is perceived from the shooting device when saidlight source is fully visible to the shooting device; and generatingsaid digital light intensity map from the preliminary digital map andthe digital image to be corrected, by determining pixels belonging tosaid light source in the preliminary digital map that are hidden fromthe shooting device in the digital image to be corrected.
 3. The methodfor correcting a halo according to claim 2, wherein the convolutionkernel is a matrix generated from a sum of a one-dimensional functionhaving a constantly decreasing envelope and representative ofcontributions from kernel scattering phenomena and a two-dimensionalfunction and representative of contributions from a kernel diffractionfigure.
 4. The method for correcting a halo according to claim 1,wherein the convolution kernel is a matrix generated from a constantlydecreasing isotropic function.
 5. The method for correcting a haloaccording to claim 4, wherein that convolution kernel is generated bymeans of the following steps: acquiring a first and a second digitaltraining image by means of the shooting device, these two imagesrespectively comprising a training light source that is switched andsaid training light source that is switched on; generating a traininglight intensity map of the training source from the first digitaltraining image by assigning a light intensity value to the pixels ofthis second digital image comprised in the light source; and calculatingthe kernel from the two digital training images and the light intensitymap.
 6. An image capture device for a three-dimensional modeling studio,comprising a plurality of shooting devices functionally connected to adata processing unit, wherein said data processing unit is speciallyadapted to implement the method for correcting a halo according toclaim
 1. 7. The method for correcting a halo according to claim 1,wherein the convolution kernel is a matrix generated from a sum of aone-dimensional function having a constantly decreasing envelope andrepresentative of contributions from kernel scattering phenomena and atwo-dimensional function and representative of contributions from akernel diffraction figure.
 8. The method for correcting a halo accordingto claim 1, wherein the convolution kernel is a matrix generated from aconstantly decreasing isotropic function.
 9. The method for correcting ahalo according to claim 2, wherein that convolution kernel is generatedby means of the following steps: acquiring a first and a second digitaltraining image by means of the shooting device, these two imagesrespectively comprising a training light source that is switched andsaid training light source that is switched on; generating a traininglight intensity map of the training source from the first digitaltraining image by assigning a light intensity value to the pixels ofthis second digital image comprised in the light source; and calculatingthe kernel from the two digital training images and the light intensitymap.
 10. The method for correcting a halo according to claim 1, whereinthat convolution kernel is generated by means of the following steps:acquiring a first and a second digital training image by means of theshooting device, these two images respectively comprising a traininglight source that is switched and said training light source that isswitched on; generating a training light intensity map of the trainingsource from the first digital training image by assigning a lightintensity value to the pixels of this second digital image comprised inthe light source; and calculating the kernel from the two digitaltraining images and the light intensity map.